Systems and methods for near-crash determination

ABSTRACT

A method for near-collision detection, including determining a risk map for a vehicle and automatically detecting a near-collision event with an object based on vehicle behavior relative to the risk map.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a is a continuation of U.S. application Ser. No.15/892,899, filed 9 Feb. 2018, which is a continuation of U.S.application Ser. No. 15/705,043 filed 14 Sep. 2017, which claims thebenefit of U.S. Provisional Application No. 62/394,298 filed 14 Sep.2016, and U.S. Provisional Application No. 62/412,419 filed 25 Oct.2016, each of which is incorporated in its entirety by this reference.

TECHNICAL FIELD

This invention relates generally to the automotive analysis field, andmore specifically to a new and useful system and method for near-crashdetection in the automotive analysis field.

BACKGROUND

Automotive safety has been a persistent issue ever since automobileswere invented. Historically, attempts at improving automotive safetyhave been focused on either improving the vehicle itself ordisincentivizing poor drivers from driving. The latter has suffered fromlack of information—until now, poor drivers could only be identifiedwhen a crash had both occurred and been recorded. Poor drivers withrisky driving habits that were causing near-collisions (e.g.,near-crashed, near-miss) or other unrecorded, high-risk situations(e.g., other's accidents, hit-and-runs) were rarely identified,penalized, or coached. Conversely, good drivers were rarely identifiedand rewarded.

Thus, there is a need in the automotive analysis field to create a newand useful system and method for near-crash determination. Thisinvention provides such new and useful system and method.

BRIEF DESCRIPTION OF THE FIGURES

FIG. 1 is a flowchart representation of the method of automaticallydetermining a near-collision event.

FIGS. 2A and 2B are a top view and a perspective view of a schematicrepresentation of a risk map.

FIG. 3 is a flowchart representation of a variation of the method ofautomatically determining a near-collision event.

FIG. 4 is a schematic representation of a risk map including a differentrisk equation for each sub-region.

FIG. 5 is a schematic representation of a risk map with one model forthe monitored region.

FIG. 6 is a schematic representation of a risk map including acontinuous function for the monitored region.

FIG, 7 is an example of how the risk map differs between a risk map fora high relative velocity (between the vehicle and the object) and a riskmap for a low relative velocity (between the vehicle and the object).

FIG. 8 is an example of how the risk map differs between a risk mapbased on a high driver score and a low driver score for a similardriving context.

FIG. 9 is an example of how the risk map differs between a risk mapbased on an object with no turning indication and a risk map based on anobject with a turning indication.

FIG. 10 is a schematic representation of data processing and transferthrough an example of the method.

FIG. 11 is an example of data that can be stored in association with thenear-collision event.

FIG. 12 is an example of determining driver attention and gazedirection.

FIGS. 13A and 13B are a front isometric and rear isometric view of anexample of the computing system, respectively.

FIGS. 14A and 14B are a first and second example of determining thecause of the near-collision event based on the host vehicle'santicipated trajectory and the external vehicle's kinematics,respectively.

FIG. 15 is a schematic representation of determining an aggregate riskmap for a geographic location using risk map from a first and secondvehicle.

FIG. 16 is a schematic representation of determining escape routes usingthe risk map.

FIG. 17 is a schematic representation of determining a recommendedtraversal route using a plurality of recorded traversal routes,including filtering out the recorded traversal routes associated withnear-collision events.

DESCRIPTION OF THE PREFERRED EMBODIMENTS

The following description of the preferred embodiments of the inventionis not intended to limit the invention to these preferred embodiments,but rather to enable any person skilled in the art to make and use thisinvention.

As shown in FIG. 1, the method for near-collision determinationincludes: determining a risk map for a vehicle S100 and automaticallydetecting a near-collision event with an object based on vehiclebehavior relative to the risk map S200.

The method is preferably performed for a physical vehicle traversingthrough a physical volume, but can be performed for a virtual model(e.g., of a vehicle) or otherwise performed. The vehicle can be: anautomobile, a motorcycle, a bicycle, a skateboard, an aerial system(e.g., a drone, plane, etc.), or be any other suitable vehicle. Thevehicle can be driven by a human driver, be automatically controlled, betelematically controlled, or be otherwise controlled. The method ispreferably performed for each of a plurality of vehicles, but canalternatively be performed for a single vehicle or any other suitableset of vehicles.

Variants of the method can confer one or more benefits over conventionalsystems. First, some variants of the method can reduce or conserve thecomputational resources and/or power consumed. In one example, themethod can monitor (e.g., determine risk metrics for) a limited regionabout the vehicle for near-collision events (e.g., only a regionencompassing the anticipated trajectory or direction of travel). In asecond example, the same model of the risk assessment module (RAM) canbe used in multiple ways (e.g., to both compute risk metric values andused to determine a cause of the near-collision event), which reducesthe number of models that need to be run, which, in turn, reduces thecomputational load. Second, some variants of the method can use aparametric model or equations for risk metric determination, which canbe advantageous because: the datasets (e.g., driving history for a givendriver) can be relatively small, and a parametric model (or equation)may account for unknown conditions better than a nonparametric model (orneural network). The parametric model can also be advantageous becauseindividual parameters' influence can be determined from the model itself(e.g., to determine cause), and can be better controlled and debugged(e.g., a managing entity can determine why the RAM generated a falsepositive or negative and correct for the false result). However, thesystem and method can confer any other suitable set of benefits.

The method is preferably performed in real- or near-real time, but allor portions of the method can alternatively be performed asynchronouslyor at any other suitable time. The method is preferably iterativelyperformed at a predetermined frequency (e.g., every millisecond, at asampling frequency, etc.), but can alternatively be performed inresponse to occurrence of a performance event (e.g., change in thevehicle attitude, change in user distraction levels, receipt of drivingsession information, receipt of new sensor information, physical vehicleentry into a geographic region associated with high collision risk,object proximity detection, etc.), be performed a single time for adriving session, be performed a single time for the vehicle, or beperformed at any other suitable frequency.

The method is preferably performed by a computing system on-board eachvehicle of the plurality of vehicles, but can alternatively be entirelyor partially performed by a remote computing system, such as a serversystem, a user device, such as a smartphone, or by any other suitableset of computing systems. The method is preferably performed using datasampled by the computing system, but can additionally or alternativelybe performed using vehicle data (e.g., signals sampled by the vehiclesensors), other vehicles' data (e.g., received from the source vehicleor a remote computing system), aggregate population data, historic data(e.g., for the vehicle, driver, geographic location, etc.), or any othersuitable data from any other suitable source.

The computing system can include a processing system (e.g., a set ofGPUs, CPUs, microprocessors, TPUs, etc.), storage system (e.g., RAM,Flash), communication system, sensor set, power system (e.g., battery,vehicle power connector, photovoltaic system, etc.), housing, or anyother suitable component. The communication system can include telemetrysystems (e.g., for vehicle-to-vehicle, vehicle-to-infrastructure,vehicle-to-remote computing system, or other communications), wirelesssystems (e.g., cellular, WiFi or other 802.11x protocols, Bluetooth, RF,NFC, etc.), wired systems (e.g., Ethernet, vehicle bus connections,etc.), or any other suitable communication systems. The sensors caninclude: cameras (e.g., wide angle, narrow angle, or having any othersuitable field of view; visible range, invisible range, IR,multispectral, hyperspectral, or sensitive along any suitablewavelength; monocular, stereoscopic, or having any suitable number ofsensors or cameras; etc.), kinematic sensors (e.g., accelerometers,IMUs, gyroscopes, etc.), optical systems (e.g., ambient light sensors),acoustic systems (e.g., microphones, speakers, etc.), range-findingsystems (e.g., radar, sonar, TOF systems, LIDAR systems, etc.), locationsystems (e.g., GPS, cellular trilateration systems, short-rangelocalization systems, dead-reckoning systems, etc.), temperaturesensors, pressure sensors, proximity sensors (e.g., range-findingsystems, short-range radios, etc.), or any other suitable set ofsensors.

In one variation, the computing system includes a set of internalsensors, a set of external sensors, and a processing system. Theinternal sensors (e.g., internal-facing camera, microphones, etc.) canbe directed toward and monitor the vehicle interior, more preferably thedriver volume but alternatively or additionally any suitable interiorvolume. The external sensors (e.g., exterior-facing camera) arepreferably directed toward the vehicle exterior, more preferably towarda region in front of the vehicle (e.g., region preceding the vehiclealong the vehicle trajectory, region proximal the driving volume andencompassing the vehicle drivetrain longitudinal vector, etc.), but canalternatively be directed toward the vehicle side(s), top, bottom, rear,or any other suitable region exterior the vehicle. The sensors arepreferably statically mounted to the vehicle and/or each other, but canbe movably mounted by a gimbal, damping system, or other motionmechanism.

In a specific example (e.g., FIGS. 13A and 13B), the computing systemincludes an interior-facing camera statically mounted at a knownorientation relative to the exterior-facing camera by a common housingand a processor electrically connected to the interior- andexterior-facing cameras, wherein the processor can be arranged withinthe common housing or outside the common housing. The processor canoptionally store a virtual mapping that associates the relative positionof one or more points (e.g., pixels) in the exterior-facing camera'sfield of view (or recorded image) with a position of one or more points(e.g., pixels) in the interior-facing camera's field of view (orrecorded image). The interior-facing camera and exterior-facing cameraare preferably concurrently operated (e.g., concurrently orsynchronously sample interior and exterior images or video,respectively), but can alternatively sample images or video at differentrates or times, sample based on the signal values of the other camera(e.g., interior-facing camera sampling is triggered when anexternal-facing camera condition, such as object detection, issatisfied), or operated at any suitable time. The common housingpreferably enables vehicles to be retrofitted with the computing system,but the system can alternatively be integrated into the vehicle. Thecommon housing preferably removably mounts to the computing system tothe vehicle, more preferably to the vehicle interior (e.g., along thedashboard, such as proximal the dashboard center region; along thewindshield, such as proximal the rear-view mirror; etc.) butalternatively to the vehicle exterior (e.g., along the hood, along theside mirrors, etc.). However, the computing system can be otherwiseconfigured and/or include any suitable set of components in any suitableconfiguration.

Determining a risk map for a vehicle S100 functions to determine acollision risk for each of a plurality of sub-regions (e.g., locations,positions) within a physical region proximal the vehicle (e.g.,monitored region). The risk map (e.g., Safety Assessment Map™ or SAM) ispreferably dynamically generated or updated in real-time, near-realtime, at a predetermined frequency, or at any other suitable time, butcan be predetermined (e.g., static) and retrieved based on a drivingparameter value (e.g., driver identifier, vehicle identifier, geographiclocation, refresh frequency, etc.), or otherwise determined. The riskmap is preferably determined by a computing system on-board the vehicle(e.g., a vehicle ECU, vehicle processor, auxiliary processor on thevehicle, etc.), but can alternatively be determined by a remotecomputing system, a local user device, or by any other suitable system,wherein the sampled sensor signals can be transmitted to the remotesystem for analysis.

The risk map preferably includes a monitored region and a risk metricfor each location or sub-region within the monitored region (exampleshown in FIGS. 2A and 2B), but can additionally or alternatively includeany other suitable information. The risk map preferably moves with thevehicle (e.g., be mobile), but can alternatively be determined for a setof geolocations (e.g., current vehicle location; locations along avehicle route; platform-specified locations, such as locations withsparse data or highly variable risk), or be associated with any suitableset of locations. The vehicle's geographic location or region can bedetermined from the vehicle location system (e.g., a GPS system, aRTK-GPS system, a trilateration system, etc.), using a method disclosedin U.S. patent application Ser. No. 15/673,098 filed 9 Aug. 2017(incorporated herein in its entirety by this reference), or using anyother suitable method.

The risk map can be dynamically generated based on parameters of:objects (e.g., external obstacles or objects, proximal objects, etc.),the operator (e.g., driver, teleoperator), vehicle itself, geographiclocation, the operating context, or any other suitable factor, whereindetermining the risk map can include determining the factor values.These factors can additionally or alternatively be used to determine themonitored region parameters (e.g., size, geometry, model types to beused, etc.), the cause of the near-collision event (e.g., elect a causefrom a set of candidate causes), or otherwise used.

The factors are preferably determined based on sensor signals sampled bythe computing system sensors, object sensors, vehicle sensors, or othersensors (wherein the method can include sampling the sensor data Siloand determining the factor values from the sensor signals S120), but canadditionally or alternatively be determined based on contextualinformation (e.g., weather) or any other suitable underlying data,wherein the method can include determining the underlying data. Theunderlying data (or derivative information, such as summaries, averages,standard deviations, etc.) can be stored (e.g., cached) permanently,temporarily, for a predetermined period of time, or for any othersuitable duration by an on-board system (e.g., vehicle, auxiliarysystem), remote system, or any other suitable system. In one variation,shown in FIG. 8, the underlying data can be cached for a predeterminedperiod of time (e.g., 1 s, 5 s, etc.), preferably by the on-board systembut alternatively the remote system, and can be erased if nonear-collision event is detected by expiration of the time period, andretained (e.g., in association with the near-collision event) if anear-collision event is detected within the time period (e.g., stored bythe recording system or processing system, transmitted to the remotesystem, etc.). However, the underlying data can be otherwise retained.

Object parameters for the object that can be used to generate the riskmap include: the object's presence, pose, kinematics, anticipatedbehavior (e.g., trajectory, kinematics, etc.), current behavior (e.g.,classification, pattern, etc.), classification or type, the object'srisk map (e.g., transmitted using V2V or V2X communications), objectidentifier, associated RAM, associated operator identifier, estimatedtime to collision (e.g., determined based on object kinematics and/oranticipated trajectory, host vehicle kinematics and/or anticipatedtrajectory, etc.), or other parameters. Object parameters (and/orassociated information) are preferably determined by a processing systemon-board the vehicle (e.g., the computing system), but can alternativelyor additionally be determined by a remote system. However, the objectparameters can be predetermined and be stored by a remote database, by adriver user device, by the vehicle, or otherwise stored, and can beretrieved on-demand, in response to access grant, or otherwise accessedor determined. Different parameters can be determined using the same ordifferent signals (e.g., different instances of the same signal type,signals sampled by different sensors, etc.), retrieved from a remotecomputing system, or otherwise determined.

The object is preferably a physical obstacle external the vehicle, butcan be otherwise defined. The object can be static or mobile. Examplesof the object include: other vehicles (e.g., automatic vehicles ormanually driven), bicycles, pedestrians, signage, curbs, potholes, orany other suitable obstacle that a vehicle can physically interact with.The object can be identified: optically (e.g., from images, video,LIDAR, etc.), acoustically (e.g., from recorded sound, ultrasound,etc.), by matching the object's known location (received from theobject) with the object's estimated location (determined based on thevehicle's location), from an object identifier (e.g., license plate,wireless identifier such as RFID, beacon identifier, etc.), or otherwiseidentified.

Object parameters can be determined based on: the vehicle's on-boardsensor signals (e.g., proximity sensors, range-finding sensors, cameras,etc.), computing system sensor signals, the object's sensor signals(e.g., wherein the signals or derivative information are transmitted tothe processing system for processing), auxiliary sensors (e.g., sensorsin the ambient environment configured to monitor object parameters, suchas security cameras, in-road weight sensors, etc.), object navigationinformation (e.g., driving instructions received from a user deviceassociated with the object), models associated with the object (e.g.,type, class), historic object behavior, or from any other suitableinformation. The object parameters can be determined using: patternmatching, computer vision techniques, parametric methods, nonparametricmethods, heuristics, rules, decision trees, Naïve Bayes, Markov, neuralnetworks, genetic programs, support vectors, or any other suitablemethod.

A first variation of determining object parameters can include detectingthe object within one or more images recorded by an external-facingcamera (e.g., still images, video, etc.). The object can be detectedusing: sensor fusion (e.g., wherein a proximity sensor indicates theobject presence and position, and segments of the image corresponding tothe object position are used to confirm or determine the objectparameters, etc.); object recognition (e.g., wherein the object detectedin the image is one of a set of predetermined or learned objects;classification; regression; pattern matching; etc.); objectidentification; image detection (e.g., image data is scanned for anobject condition); scene matching (e.g., an object is detected bycomparing the image to a reference image of the same scene for the samegeographic location); or any other suitable method. Examples of objectrecognition approaches that can be used include: a geometric approach;photometric approach; a neural network (e.g., CNN); object model-basedmethods (e.g., edge detection, primal sketch, Lowe, recognition byparts, etc.); appearance-based methods (e.g., edge matching, divide andconquer, grayscale matching, gradient matching, histograms of receptivefield responses, HOG, large model bases); feature-based methods (e.g.,interpretation trees, hypothesize and test, pose consistency, poseclustering, invariance, geometric hashing, SIFT, SURF, bag of wordsrepresentations, Viola—Jones object detection, Haar Cascade Detection);genetic algorithms; or any other suitable approach. In a first example,detecting the object can include generating a HOG image from the frame(e.g., using a global analysis module, object-specific analysis module),matching the extracted HOG pattern with a predetermined HOG pattern fora set of objects, projecting and/or posing the objects (e.g., usingobject landmark estimation, affine transformation, and/or other featureidentification methods or transformations), encoding the image using anembedding or set of measurements (e.g., using a CNN trained on images ofthe object(s)), and identifying an object based on the embedding values(e.g., using a classifier, such as a SVM classifier). In a secondexample, determining the object type includes: extracting an objectshape from the sensor measurements and classifying the object shape todetermine the object type. However, the object can be otherwise detectedand/or identified. The object can be given a number, associated with aspecific equation, associated with a specific parametric weight, orotherwise influence risk score calculation.

A second variation of determining object parameters can includedetermining the object pose (e.g., relative position, distance, angle,orientation, etc. relative to the camera, vehicle, or sensor) using poseestimation techniques from images recorded by the external-facingcamera. The pose is preferably determined using analytic or geometricmethods (e.g., using a set of known object geometries retrieved based onthe detected object class or type, object markings such as vehiclebadging, or other features), but can alternatively be determined usinggenetic algorithm methods, learning-based methods, or any other suitablemethod. The matched object geometries or libraries can be: all availableobject geometries, geometries for objects associated with the drivingcontext (e.g., no human geometries or modules for images recorded on ahighway), or otherwise limited. For example, a HOG image, generated froman image region with a high probability of including a projection of avehicle, can be matched to predetermined HOG patterns for a set of posesfor a vehicle (e.g., generic vehicle, specific vehicle make and model,etc.), wherein the pose associated with the matched predeterminedpattern can be assigned to the detected vehicle. In a second example,the external-facing cameras are a stereo camera pair, wherein the objectdistance from the vehicle can be determined based on the disparitybetween the images recorded by the stereo camera pair. However, theobject pose, or elements thereof, can be otherwise determined.

A third variation of determining object parameters can includedetermining object kinematics. This can include applying motionestimation methods to the images (e.g., external videos, first video,etc.) recorded by the external-facing camera, such as direct methods(e.g., block-matching techniques, phase correlation and frequency domainmethods, differential methods, such as Lucas-Kanade, Horn-Schunck,Buxton-Buxton, Black-Jepson, variational methods, discrete optimizationmethods, pixel recursive algorithms, optical flow methods, etc.),indirect methods (e.g., corner detection, RANSAC, etc.), or any othersuitable method; using radar or another range-finding system; orotherwise determined. In one example, determining object kinematicsincludes: identifying the object in a first sensor signal frame (e.g.,image, video frame); tracking the objects across multiple subsequentframes; determining the relative object trajectory and/or kinematics(e.g., acceleration, velocity) based on the relative object locationwithin each frame and each frame's timestamp; determining the hostvehicle trajectory and/or kinematics associated with each frame (e.g.,based on concurrently-recorded orientation sensor data, based on opticalflow techniques, etc.); and determining the object kinematics based onthe relative object kinematics and the host vehicle kinematics. In asecond example, determining the object kinematics includes: extracting amotion pattern from a series of images, and determining the kinematicsparameter values by classifying or pattern matching the motion pattern.However, the object kinematics can be determined based on the vehicle'skinematics (e.g., measured using the auxiliary system, the user device,received from the vehicle, etc.), the object's kinematics (e.g.,received from the object, estimated from sequential sensor measurementsof the object, etc.), or be otherwise determined. However, the objecttrajectory or kinematics can be otherwise determined.

A fourth variation of determining object parameters can includedetermining the anticipated behavior of the object. The anticipatedbehavior can be determined based on historic behavior for the object (orsimilar objects), pattern recognition, predetermined behaviors mapped tothe object parameter values, predetermined behaviors mapped toprecipitating factors detected in the sensor signals, or using any othersuitable data or method. In one embodiment, determining the object'santicipated behavior includes retrieving an anticipated behavior for theobject based on object parameters, such as object class or object pose.For example, a leading vehicle can be expected to move forward at thespeed limit or a historic speed for the vehicle (e.g., retrieved usingthe vehicle's license plate number). In another example, a pedestrian atan intersection that is looking at the host vehicle (e.g., based on eyetracking methods run on the external image) can be anticipated to crossthe street. In a second embodiment, pattern recognition methods can beapplied to the object's historic path (e.g., as determined from a seriesof sensor measurements), wherein the object's anticipated motion can bedetermined from the recognized pattern. In a third embodiment, theobject's historic behavior for the same location or a similar drivingcontext (e.g., similar weather conditions, number of intersections,distribution of objects, etc.) can be used as a proxy for the object'santicipated behavior. In a fourth embodiment, the object's anticipatedmotion can be a predetermined anticipated motion mapped to aprecipitating factor extracted from the sensor signal. Precipitatingfactors can include ego-motion (e.g., actions), indicators, V2Vcommunications, or any other suitable factor that are associated withinitiation of object movement. Examples of precipitating factorsinclude: visual indicators, such as lead vehicle brake light operation(e.g., associated with lead vehicle deceleration, determined from afront-facing sensor stream, etc.), external vehicle turn indicatoroperation or turned wheels (e.g., associated with external vehicletranslation toward the side associated with the turn indicator or wheelturn), swerving (e.g., associated with higher external vehicletrajectory uncertainty), pedestrian body part into the intersection(e.g., associated with pedestrian street crossing); sensor signalpatterns (e.g., external vehicles' steering wheel position sensorsignals, brake position, accelerator position, selected gear, etc.); orany other suitable precipitating factor. The precipitating factor andcorresponding anticipated motion can be manually associated,automatically associated (e.g., learned using a supervised orunsupervised training set), or otherwise determined. However, theobject's anticipated motion can be determined from the externalvehicle's navigation system (e.g., from an app on the driver's userdevice, from a central navigation system, etc.), from the controlalgorithm used by the vehicle OEM, from the external vehicle itself,from the most probable paths historically taken by objects in thatlocation or region, or otherwise determined.

A fifth variation of determining object parameters can includedetermining the object parameter from secondary sensor information(e.g., proximity sensor information, range-finding information, radarinformation, etc.) recorded within a predetermined time window of imagerecordation. Parameter values extracted from different sensor sourcesfor the same object can be correlated using odometry, timestamps, or anyother suitable association. However, the object parameters can beotherwise extracted or determined.

Operator parameters (user parameters) that can be used to generate therisk map include: operator profiles (e.g., history, driver score, etc.);operator behavior (e.g., user behavior), such as distraction level,expressions (e.g., surprise, anger, etc.), responses or actions (e.g.,evasive maneuvers, swerving, hard braking, screaming, etc.), cognitiveability (e.g., consciousness), driving proficiency, willful behavior(e.g., determined from vehicle control input positions), attentiveness,gaze frequency or duration in a predetermined direction (e.g., forwarddirection), performance of secondary tasks (e.g., tasks unrelated todriving, such as talking on a cell phone or talking to a passenger,eating, etc.), or other behavior parameters; or any other suitableoperator parameter. The operator can be the operator of the hostvehicle, the operator of the object(s) or vehicle(s), or be any othersuitable operator.

The operator behavior can be characterized as a behavior class or type,a behavior score (e.g., calculated based on the operator distractionlevel, expressions, etc.), or otherwise characterized. The operatorbehavior is preferably determined from the operator-monitoring sensorsignals (e.g., internal-facing camera video), but can be backed out ofthe determined vehicle ego-motion or otherwise determined. The operatorbehavior can be identified and/or characterized using rules (e.g.,within a time window from the near-collision event), heuristics,decision trees, support vectors, probabilitistics (e.g., Naïve Bayes),neural networks, genetic programs, pattern matching (e.g., patterns ofone or more sensor data sets), or any suitable method. The operatorprofile can be the driver profile associated with a vehicle identifierfor the respective vehicle (e.g., external vehicle, host vehicle),wherein the vehicle identifier can be determined from sensormeasurements recorded by sensor on-board the vehicle (e.g., licenseplate number extracted from the external-facing camera), the vehicleidentifier associated with the computing system, or otherwisedetermined; be the operator profile associated with a geographiclocation collocated with the object; be the operator profile associatedwith the driving session or timeframe (e.g., a scheduled driver for thevehicle); be the operator profile associated with a user identifier(e.g., dongle identifier, user device identifier, face, etc.), or be anyother suitable operator profile. The operator profile is preferablyautomatically generated based on historic vehicle operation data (e.g.,recorded during past driving sessions), such as past risk maps, but canalternatively be manually generated (e.g., by the operator, by a fleetor system management entity) or otherwise generated. The operatorprofile can include the operator's risk score (e.g., calculated based onpast risk maps, near-collision history, tailgating history, distractionhistory, collision history, etc.), routes, operator identifier, operatordriving schedule, RAM, or any other suitable information.

The operator behavior can be determined from sampled signals monitoringthe vehicle interior, or be otherwise determined. In one variation, theoperator behavior can be determined from images recorded by aninterior-facing camera (e.g., interior video, second video, etc.). Theinterior-facing camera is preferably directed toward the driver volume,but can alternatively be directed toward the entirety of the interior,or to any suitable volume. In one example, operator attention to adetected object can be determined based on an operator's gaze directionrelative to the object (e.g., whether the operator is looking at theobject) based on the interior-facing sensor signals, the exterior-facingsensor signals, and the known relative orientation of the interior- andexterior-facing sensors. In a specific example (e.g., FIG. 12), theoperator attention can be determined by: determining the operator gazedirection relative to the vehicle (e.g., using eye tracking methods)from the interior image, determining the exterior object positionrelative to the vehicle from the exterior image, mapping the operatorgaze direction to an exterior gaze region using the known relativeorientation between the interior-facing camera and exterior-facingcamera, assigning a high attention score (or determining that theoperator has seen the object) when the exterior gaze region encompassesthe exterior object position(s). However, the operator attention can beotherwise determined. In a second variation, the interior images can beanalyzed for operator emotion (e.g., surprise) using emotion expressionrecognition techniques. In a third variation, the sensor signals orvehicle control input positions can be analyzed for patterns indicativeof operator behavior (e.g., swerving, sudden braking, etc.). However,the operator behavior can be otherwise determined.

Vehicle parameters that can be used to determine the risk map caninclude: vehicle kinematics (e.g., acceleration, jerk, velocity, etc.),mass, class, make or model, wear, age, control input position (e.g.,brake position, accelerator position, transmission position, etc.),current geographic location (e.g., using on-board location systems),past geographic locations or driving route, anticipated driving route(e.g., determined from a navigation system, historic routes, etc.),vehicle position relative to lane markings or other road markings, orother vehicle parameters. Vehicle kinematics can be determined usingoptical flow methods, on-board kinematic sensors such as accelerometersor IMUs, location sensors, or otherwise determined. Vehicle parameterscan be pre-associated with the computing system or set of sensorsmonitoring the driving session, be vehicle parameters associated with avehicle identifier for the host vehicle, be parameters determined basedon sensor signals sampled during the driving session, or be otherwisedetermined.

Geographic location parameters that can be used to generate the risk mapinclude: the location's risk profile (e.g., collision risk mapassociated with the geographic location or region), the location'straffic regulations (e.g., speed limit, retrieved from a database,etc.), the location's traffic conditions (e.g., determined from thedensity of computing systems located in the region, from historictraffic, etc.), the road type (e.g., urban, highway, etc. determinedbased on the vehicle location and a database, etc.), the road conditionor construction (e.g., determined from public reports, historic driverreports, inferred from other drivers' sensor data, etc.), roadwayinfrastructure, traffic signs (e.g., determined from a predeterminedmap, from images sampled by the external-facing camera, etc.), roadmarkings (e.g., lane markings, etc.), the RAM associated with thelocation, or any other suitable geographic location information. In aspecific example, intersections can be associated with different riskassessment models from highways. The geographic location is preferablythe host vehicle's current geographic location (e.g., determined byon-board location systems), but can alternatively be the host vehicle'spast or anticipated geographic location, or be any other suitablegeographic location. The geographic location parameters can be retrievedfrom a remote database (e.g., from the remote computing system), storedon-board the computing system, or be otherwise accessed. The geographiclocation parameters can be determined in real- or near-real time (e.g.,based on on-board sensor signals, V2X communications, etc.),asynchronously, or otherwise generated. The geographic locationparameters can be manually generated, automatically generated (e.g.,based on one or more vehicles' operation parameters, aggregated frommultiple vehicles or passes), generated from maps, or otherwisedetermined.

Operating context parameters that can be used to determine the risk mapinclude: traffic density, time of day, weather, ambient lighting, wheeltraction, visual obstructions, or any other suitable contextualparameter. The contextual parameter can be retrieved from an externaldatabase S130, measured using on-board sensors, or otherwise determined.Operating context parameters can optionally include computing systemoperational parameters, such as available computational power, availablepower (e.g., the computing device battery's state of charge), availablememory, or any other suitable parameter.

The risk metric is preferably indicative of a collision risk for eachsub-region within the monitored region, but can additionally oralternatively be indicative of the probability of a collision within therespective sub-region, the vehicle's safety within the respectivesub-region, or be indicative of any other suitable parameter. The riskmetric can be a continuous function extending across multiple locationswithin the monitored region (examples shown in FIGS. 6), be a discretescore for each discrete sub-region (example shown in FIG. 4), or beotherwise determined. For example, the risk assessment module (RAM) caninclude an equation, wherein only the risk score for a location proximalthe external object can be calculated using the equation. Risk scoresfor other sub-regions within the monitored region can be calculated inresponse to the first risk score exceeding a threshold value. The riskmetric can be aligned with the region orientation or otherwise oriented.The risk metric can be updated: in real- or near-real time (e.g., assensor data is sampled or received, as factor values are determined,etc.), at a predetermined frequency, in response to occurrence of apredetermined event (e.g., object entering the monitored region), bestatic (e.g., predetermined), or be determined at any other suitabletime. The risk metric for each sub-region is preferably determined basedon one or more of the factors discussed above, but can be otherwisedetermined. The risk metric can be determined: heuristically, using apredetermined rule, calculated (e.g., using an equation), using anartificial neural network (e.g., CNN, DNN, etc.), decision tree,clustering, Bayesian network, or be otherwise determined.

However, the risk map can be determined in any suitable manner using anyother suitable set of factors.

The monitored region is preferably a physical volume or area proximalthe vehicle that is monitored for near-collision events, but can beotherwise defined. The monitored region is preferably virtuallymonitored, but can alternatively or additionally be physically monitored(e.g., using on-board vehicle sensors), or otherwise monitored.Virtually monitoring the monitored region preferably includes generatinga virtual risk map encompassing a virtual region corresponding to thephysical region, but can alternatively or additionally include a virtualscene representing the physical region (e.g., including representationsof the detected objects), a risk score for the entire physical region, arisk vector (e.g., denoting the direction of highest collision risk(s)for the vehicle), or otherwise virtually monitoring the monitoredregion.

The monitored region (and/or virtual region, wherein monitored regiondescriptions can hereinafter also apply to virtual regions) can beassociated with region dimensions, region pose (e.g., relative to thevehicle, an object, a non-vehicle point), or any other suitable regionparameter. The monitored region can optionally be associated with a riskpoint distribution, RAM(s), or any other suitable data.

The region dimensions can influence how early a precipitating event(e.g., near-collision event, event preceding the near-collision event)is detected. The region dimensions can additionally or alternativelyinfluence which objects are considered in the risk assessment. Forexample, objects outside of the monitored region can be disregarded. Theregion can be 2D, 3D, 4D (e.g., spatiotemporal), or have any suitablenumber of dimensions. The region dimensions can include: a regiongeometry (e.g., shape), area, critical dimension (e.g., radius, height),or other suitable set of dimensions. The region geometry is preferably acircle or sphere but can alternatively be a conic section, polygon,sector, cone, pyramid, prism, amorphous, or have any other shape. Theregion can be symmetric or asymmetric in one or more axis (e.g., x, y,z).

The monitored or virtual region pose (e.g., position and/or orientation)relative to the vehicle functions to limit the area or volume ofmonitored space. The monitored region can encompass the vehicle (e.g.,surround the vehicle, be centered about the vehicle, be offset from thevehicle, etc.), extend from the vehicle, abut or be adjoined with thevehicle, trace a vehicle profile, be next to the vehicle (e.g., touchingthe vehicle, be separated from the vehicle by a non-zero distance), orotherwise related to the vehicle. In one example, the region can beseparated from the vehicle by a distance substantially equal toproximity sensor(s)' sensitivity distance(s), wherein the computingsystem can be used to monitor collision risk for distal obstacles andthe proximity sensors used to monitor collision risk for proximalobstacles. The monitored region preferably encompasses a subset of thevolume surrounding the vehicle, but can alternatively encompass theentirety of the volume surrounding the vehicle, the entirety of thesuperterranian volume surrounding the vehicle, or encompass any suitablevolume. In one example, the monitored region excludes a region proximalthe vehicle rear. In a second example, the monitored region encompassesa region preceding (or in front of) the vehicle. In a third example, themonitored region encompasses a region encompassing the possible hostvehicle trajectories (e.g., immediately possible, trajectories possiblewithin a predetermined time duration, which can be selected based on thevehicle kinematics, etc.). In a fourth example, the region is defined bya predetermined geo-fence. However, the monitored region can beotherwise positioned relative to the vehicle. The monitored region canadditionally or alternatively encompass, be adjacent to, or otherwise bephysically associated with the object(s).

The region orientation is preferably aligned (e.g., centered, parallel,coaxial, etc.) along the instantaneous or anticipated direction of hostvehicle travel, but can alternatively be aligned with the vehiclecenterline (e.g., longitudinal centerline), aligned with a predeterminedvector relative a vehicle reference point (e.g., relative to thelongitudinal centerline), aligned with a vector representing the highestrisk trajectory, or otherwise aligned. The instantaneous or anticipateddirection of host vehicle travel can be determined from: the steeringwheel position, the wheel (e.g., tire) positions, past kinematic data(e.g., sampled within a predetermined time window with the kinematicsensors), optical flow data (e.g., from images sampled by the cameras),navigation information (e.g., retrieved from an operator user device,vehicle control instructions, etc.), historic route information (e.g.,for the operator, vehicle, etc.), or otherwise determined. Theinstantaneous or anticipated direction of host vehicle travel can bedetermined using: pattern matching, rules, decision trees, Naïve Bayes,neural networks, genetic programs, support vectors, or any othersuitable method.

The risk point distribution functions to specify the sub-regions (e.g.,points, sub-areas, sub-volumes, etc. and/or locations thereof) withinthe monitored region for which risk values will be determined. Thesub-regions (for which risks are determined) can have the same ordiffering geometry, size, or other parameter. The risk pointdistribution preferably forms a continuous space (e.g., topological,volumetric), but can alternatively or additionally form a discrete spacee.g., topological, volumetric), form a partially continuous andpartially discrete space, or any other suitable space. The risk pointdistribution within the discrete space can be uniform, random,non-linear (e.g., quadratic, logarithmic, exponential, etc.), linear, asingle sub-region, or otherwise distributed. For example, the risk pointdistribution can have a higher point density proximal the vehicle,higher point density proximal the anticipated trajectory, have a pointdensity that varies as a function of the collocated or respective riskscore, etc.), or have any suitable distribution. The risk pointdistribution can be determined based on the monitored region parameters(e.g., different distributions for different parameter valuecombinations), RAM, the factor value(s), be a default distribution, orbe otherwise determined. Additionally or alternatively, the region maynot include a risk point distribution, and include a binarydetermination (e.g., whether an object is detected within or anticipatedto enter the monitored space), a risk score for the monitored space, arisk vector (e.g., summed from the risk vectors associated with thefactor values), or be associated with any other suitable risk metric.

The monitored region parameters can be dynamically adjusted (e.g., basedon up-to-date sensor information), static, or otherwise determined S140.The monitored region parameters can be universal, specific to acombination of factor values, specific to an operator or vehicle, orotherwise shared. The region parameters are preferably determined basedon factor values (e.g., from signals sampled within a time window ofregion parameter determination, such as several seconds or minutes), butcan be otherwise determined. Dynamically adjusting the monitored regionparameters can function to reduce or conserve the computational powerwhile monitoring the physical areas that should be monitored forcollisions, given the context. This can function to reduce overall powerconsumption, which can be desirable in applications where the processingsystem is powered using batteries or another limited power source (e.g.,in an auxiliary, battery-powered system, in electric vehicles, etc.).The monitored region parameters can be determined: heuristically, usinga predetermined rule, calculated, using an artificial neural network(e.g., CNN, DNN, etc.), decision tree, clustering, Bayesian network, orotherwise determined. The monitored region parameters can be determinedbased on sensor signals (e.g., images, accelerometer data, vehiclesensor data, etc.), driver profiles (e.g., historic habits, risk score),location data (e.g., traffic regulations, road type, road condition,etc.), object behavior (current or anticipated), derivative informationthereof, or based on any other suitable information.

The region dimensions can be static (e.g., predetermined) or bevariable. In the latter variant, the region dimensions can be selected(e.g., from a predetermined library associating factor and dimensionvalues) or calculated based on the values of one or more of the factorsdescribed above, as a function of time, as a function of near-collisionevent frequency, as a result of the RAMs used for risk map generation(e.g., wherein the RAMs are each associated with a set of regiondimensions, wherein the resultant region dimensions are an aggregate ofsaid dimensions), or vary in any other suitable manner. For example, theregion dimensions can change as a function of host vehicle speed (e.g.,increase with increased vehicle speed), vehicle geographic location(e.g., wherein each geographic location or associated collision risk mapor score can be associated with a set of region dimensions), anticipatedobject behavior (e.g., for the lead vehicle, adjacent vehicle,pedestrian, etc.), or any other suitable factor.

In a first example, the region size or area can increase with increasedgeographic risk. In a second specific example, the region shape can beadjusted (or a new shape selected) to be biased toward the right whenthe lead vehicle is anticipated to move right (e.g., based on rightindicator operation). In a third example, the monitored region sizeincreases with the vehicle's velocity, road speed limit, road type(e.g., increases for highways, decreases for urban streets), and driverrisk, and decreases with increased traffic along the route. In aspecific example, the method includes determining a following distancefor the driver based on the instantaneous parameter values, and sets themonitored region size at the determined following distance. In a fourthexample, the number of sub-regions within the monitored region for whichrisk scores are determined decreases with increased velocity, and thearea encompassed by each sub-region increases. In a third example, themonitored region shape changes based on the location's profile. In aspecific example, the monitored region shape can be a circle forlocations with high side impact frequencies or slow side traffic (e.g.,pedestrian or bicycle traffic), and be a sector for locations with lowside impact frequencies (e.g., highways). In a fourth example, themonitored region size increases with the driver's risk score. However,the monitored region parameters can be otherwise determined.

Determining the risk map S100 can include determining a risk metric forthe monitored region S160. The risk metric can be determined for theentire monitored region, one or more sub-regions of the monitored region(e.g., a risk point), or for any other suitable region. The risk metricis preferably a risk score, but can be a risk probability or be anyother suitable metric. The risk metric is preferably determined by aRAM, but can be determined by any suitable system.

The risk map can be: an array of risk metric values (e.g., for eachsub-region identifier), a heat map (e.g., stored or visualized as a heatmap), an equation, or be otherwise structured. The risk map(s) orparameters thereof (e.g., RAM, factor values, weights, geolocations,etc.) can be stored temporarily (e.g., long enough to analyze theinstantaneous risk), for the driving session duration, for longer thanthe driving session, or for any suitable time. All or a subset of thegenerated risk maps or parameters thereof can be stored. The risk maps(or parameters thereof) can be stored in association with the respectivevehicle identifier, geographic location or region identifier, operatoridentifier, vehicle kinematics, or any other suitable factor values.

The risk assessment module (RAM) associated with the monitored regionfunctions provide a model or method to determine the risk metric for themonitored region. The RAM preferably determines a risk score for eachrisk point within the risk point distribution (e.g., populate the riskmap), but can alternatively or additionally determine a risk score for asubset of the risk points within the distribution, a risk score for themonitored region, a risk score for the instantaneous driving context, orany other suitable risk metric for any other suitable region or context.Each risk point within a monitored region can be associated with thesame RAM (e.g., FIG. 5) or different RAMs (e.g., FIG. 4). The RAMpreferably includes a continuous function, but can alternatively oradditionally include a discretized function or any other suitablefunction. The RAM preferably includes a parametric model (e.g., be aparametric module), but can alternatively be a nonparametric model,semi-parametric model, semi-nonparametric model, or include any othersuitable model. The RAM can include one or more models. The RAMpreferably includes a set of equations (e.g., one or more probabilitydistributions), but can alternatively be a neural network (e.g., CNN),support vector, decision tree, set of rules, classifier (e.g., Bayesianclassifier), genetic program, or be otherwise structured. For example,the RAM can include: a discrete probability distribution, a continuousprobability distribution, normal distribution (e.g., Gaussiandistribution, such as a 2D Gaussian or 3D Gaussian, multivariate normaldistribution, etc.), log-normal distribution, Pareto distribution,discrete uniform distribution, continuous uniform distribution,Bernoulli distribution, binomial distribution, negative binomialdistribution, geometric distribution, hypergeometric distribution,beta-binomial distribution, categorical distribution, multinomialdistribution, Tweedie distribution, Poisson distribution, exponentialdistribution, gamma distribution, beta distribution, Rayleighdistribution, Rice distribution, or any other suitable riskdetermination model. The risk distribution can be centered or have anapex at the external object, at the vehicle, or at any other suitablelocation. In one example, the risk model includes an equation with a setof weighted factors. However, the model can be otherwise configured. TheRAM can include one or more models. Each monitored region can beassociated with one or more RAMs at a given time, and can be associatedwith the same or different RAMs over time (e.g., over a drivingsession).

The RAM preferably uses the factor values to determine the risk, but canalternatively use other values. For example, the risk for eachsub-region of the risk map can be determined based on the objectparameters and the operator behavior score. However, the risk for eachsub-region can be otherwise determined.

Each RAM is preferably static, but can alternatively be dynamicallyadjusted (e.g., in real- or near-real time, as factor values aredetermined, etc.), adjusted at a predetermined frequency, adjusted inresponse to occurrence of an event (e.g., through an update), orotherwise adjusted, wherein the method can include generating the RAM.The RAM (e.g., models, weights, factors, etc. therein) can be manuallygenerated, automatically generated (e.g., using supervised orunsupervised learning, such as using a set of time-series data labeledwith near-collision labels or collision labels, etc.), generated using aneural network or other machine learning algorithm, generatedempirically or heuristically, dynamically generated (e.g., whereinfactor weights are populated based on a secondary factor's value), orotherwise determined.

The system can include a universal RAM or multiple RAMs, whereindifferent RAMs can be associated with different monitored regions (e.g.,type, class), monitored region parameters (e.g., shape, size,orientation, bias), operator profiles, vehicle profiles, computingsystems, geographic locations or regions (e.g., geo-fences), objectparameters, driving contexts, specific values for other factors,specific factor value combinations (e.g., scenario class, register,etc.), or any other suitable set of data. When the system includesmultiple RAMs, the method can include determining the RAM.

In a first variation, a single RAM (e.g., equation) can be used tocalculate all risk scores (e.g., risk maps) in all registers (e.g.,contexts).

In a second variation, different RAMs are used to calculate risk metrics(e.g., risk maps) in different registers and/or overlaid when differentregisters concurrently occur. The RAM to be used is preferablydetermined based on the factor value(s), but can be determined based onthe monitored region parameter(s) or otherwise determined. The factorvalues used to determine which RAM to use can be the same or differentfactors as those fed into the RAM to determine the risk metric (e.g.,risk maps). The RAM is preferably selected from a predetermined librarybased on one or more factor values (e.g., wherein the risk metric isdetermined for each sub-region within the monitored region using theselected RAM), but can alternatively be dynamically generated (e.g.,model type(s) selected; weights calculated, selected, or otherwisedetermined; etc.), or otherwise determined S150. In a first embodiment,the RAM is selected based on the geographic location identifier, theaggregate risk map associated with the geographic location, thegeographic location parameters (e.g., traffic density, pedestriandensity, intersection presence, average speed, speed limit, etc.),and/or any other suitable geographic location data. In a secondembodiment, the RAM is selected based on the host vehicle operationparameters. For example, different modules (or weights or factors usedtherein) can be selected for different vehicle acceleration orvelocities. In a third embodiment, different operators are associatedwith different RAMs (and/or monitored region parameters). For example,an operator with a high driver score or reaction time can have a lowweight assigned to unnoticed proximal objects, while an operator with alow driver score can have a high weight assigned to the same object. Ina fourth embodiment, the RAM is selected based on the object parametervalues (e.g., class, distance, anticipated trajectory, kinematics,operator profile, etc.). In this embodiment, a RAM can be determined foreach object that is detected, wherein the multiple RAMs can be combined(e.g., overlaid, etc.) to cooperatively form a composite RAM used tomonitor the region. In a specific example, the method can includeselecting a first equation in response to detection of a leadingvehicle, select a second equation in response to detection of a bicyclelocated diagonally from the vehicle, and select a third equation inresponse to anticipated leading vehicle turning. However, the RAM can beotherwise determined.

In a first variation, determining the risk metric for the monitoredregion includes calculating a risk score for each of a plurality ofsub-regions within the monitored region. The risk score can becalculated by an on-board system, a remote system, or by any othersuitable system. The risk score can be calculated using an equation withweighted factors, but can be otherwise calculated.

In one example, the risk score for a sub-region can be determined basedon the presence and type of object, the object's kinematics relative tothe vehicle (e.g., FIG. 6), the operator profile of the object (e.g.,FIG. 8), the anticipated action of the object (e.g., FIG. 9), and thehost vehicle operator's behavior score (e.g., attention or distractionlevel, etc.). However, the risk score can be otherwise calculated fromany other suitable equation.

In a second variation, each sub-region within the monitored region canbe associated with a different equation, wherein the scores for eachsub-region can be independently calculated. In this variation, themethod can include: monitoring the monitored region for an object,identifying the sub-region(s) coincident with the object, calculatingthe risk score for each of the identified sub-region(s) (e.g., based onthe object parameters), and determining the near-collision event basedon the calculated risk scores. However, the equations per sub-region canbe otherwise used.

In a third variation, the method can include determining the risk scorefor each sub-region using a neural network. In this variation, theentire monitored region is preferably treated as a single region,wherein a single neural network determines the risk score for eachsub-region. However, each sub-region within the monitored region can beassociated with a different neural network that determines theassociated risk score.

In a fourth variation, the method can include generating a map of thevehicle's environment and tracking the vehicle's location within theenvironment (e.g., using simultaneous localization and mapping),optionally classifying the identified objects as static or mobile,determining potential vehicle movement paths to each of the sub-regions(e.g., using RRT), and determining a collision probability for eachsub-region based on the map and potential vehicle movement paths.

However, the risk score can be determined using a combination of theaforementioned methods, or otherwise determined.

Automatically detecting a near-collision event S200 functions toidentify high-risk events. A near-collision event (near-collision event)can be a circumstance requiring an evasive maneuver by the vehicleoperator; a circumstance wherein the vehicle has above a thresholdprobability of colliding with an object; or be otherwise defined.

The near-collision event is preferably determined in real- or near-realtime (e.g., as the event is occurring, before the event occurs), but canbe determined asynchronously or at any other suitable time. Thenear-collision event is preferably determined by a system on-board thevehicle (e.g., by an auxiliary system, the vehicle itself, etc.), butcan alternatively be determined by a remote computing system or anyother suitable system. The near-collision event is preferablyautomatically detected based on the risk map, but can be otherwisedetermined. The near-collision event is preferably detected using theconcurrent risk map (e.g., the risk map generated within a predeterminedtime period before the near-collision event, the risk map generated forthe time during which the near-collision event was detected, etc.), butcan be detected using a prior risk map, a series of prior risk maps, orusing any other suitable set of risk maps.

In a first variation, the near-collision event is detected when ahigh-risk region in the risk map overlaps the object location(s)(example shown in FIG. 10). The high-risk region can be a region (e.g.,area, sub-region, point, position, virtual or geographic location) ofthe monitored region with risk value(s) exceeding a threshold risk value(e.g., risk score), a predefined area of the monitored region assignedas the high-risk area, or otherwise defined. The threshold risk valuecan be determined (e.g., calculated, selected, etc.) based on theoperator profile or score, the historic location risk, operator profilesor scores for proximal objects, or any other suitable factor.

In a second variation, the near-collision event is detected in responseto host vehicle movement toward (e.g., the anticipated trajectory orcurrent direction of travel intersects or is pointed toward) or into thehigh-risk area (e.g., before, during, or after high-risk areaidentification). In this variation, the risk map can remain staticrelative to the geographic location for which the risk map wasgenerated, move with the host vehicle, or have any other suitable set ofdynamics.

In a third variation, the near-collision event is detected when thevehicle's motion relative to the high-risk area (e.g., within the riskmap) substantially matches a near-collision event pattern (e.g.,determined based on historic near-collision event patterns), isclassified as a near-collision event (e.g., based on the spatial riskmetric pattern, temporal risk metric pattern, etc.), or is otherwiseassociated with a predetermined near-collision event.

In a fourth variation, the near-collision event is detected in responseto a risk score within the risk map exceeding a threshold risk score.The near-collision event can be detected immediately upon the riskexceeding the risk score threshold, be detected after the risk exceedsthe threshold for a threshold duration, be detected if spatiallyadjacent risk scores (e.g., a threshold number, threshold distribution,threshold physical or virtual volume or area, etc.) exceed the thresholdvalue, or otherwise detected based on the risk scores within themonitored region. The threshold risk score can be manually selected,automatically determined (e.g., learned from driving sessions labeledwith near-collision events), or otherwise determined.

In a fifth variation, the near-collision event is detected in responseto the risk score within a threshold distance of the vehicle exceeding athreshold risk value, wherein the monitored region preferablyencompasses the threshold distance but can alternatively be otherwiserelated to the threshold distance. The threshold distance is preferablymeasured along the vehicle traversal vector, but can alternatively bewithin a predetermined angular range of the vehicle traversal vector,within a threshold width of the vehicle traversal vector (e.g., thevehicle body's width), be a radius about the vehicle body or center, orotherwise defined. The threshold distance can be predetermined;dynamically determined based on (e.g., vary as a function of): the userattentiveness, cognitive ability, reaction time, distraction level,vehicle speed, object trajectory, kinematics, or distribution, orotherwise determined. For example, the near-collision event can bedetected in response to the risk score within 5 ft in front of thevehicle exceeding a threshold risk score.

In a sixth variation, the near-collision event is detected in responseto the total area or volume of sub-regions within the monitored regionhaving risk scores exceeding a threshold risk score exceeding athreshold area or volume.

In a sixth variation, the near-collision event is detected in responseto a pattern of sequential risk maps for a vehicle substantiallymatching (e.g., consistent with) a pattern associated with anear-collision event. However, the near-collision event can be otherwisedetermined.

In a seventh variation, the near-collision event is detected based onsignals sampled by sensors on-board the vehicle (e.g., auxiliary systemsensors, vehicle sensors, proximity sensors, etc.), vehicle parameters(e.g., acceleration pedal position, steering wheel position, brakeposition, etc.), external vehicle sensor signals, or based on any othersuitable measurement, using pattern matching (e.g., wherein the sensorsignal pattern matches a pattern associated with a near-collisionevent), neural networks, rules, or using any other suitable method. Forexample, the near-collision event can be detected when a decelerationspike is detected in the kinematic sensor measurements, when a surpriseddriver expression is detected from an interior-facing camera stream,when a kinematic pattern substantially matches a “swerving” pattern(e.g., based on the vehicle's sensors, such as brake pedal position;based on the system's accelerometer, gyroscope, or IMU measurementsindicating a G-force exceeding a predetermined threshold; based onimages recorded by the recording system; the lateral accelerationexceeds a threshold acceleration; etc.), when the brakes are suddenlyapplied, when an object occupies more than a threshold proportion of anexternal-facing camera's field of view, when screeching is detected(e.g., from the audio sensor), when a collision is detected (e.g.,wherein the sensor data sampled before the collision time is associatedwith a near-collision event; wherein a collision is detected in responseto the measured G-force exceeding a collision threshold, in response tothe acoustic pattern substantially matching a collision pattern, inresponse to the airbags deploying, or otherwise determined), or when anyother suitable condition associated with a near-crash event is detected.

However, the near-collision event can be otherwise determined.

The method can optionally include storing parameters of thenear-collision event S210. Near-collision event parameters can include:a near-collision event time (e.g., detection time, sampling timestamp ofthe underlying data, etc.), the vehicle location during thenear-collision event (e.g., received from the location sensor of theon-board system), parameters of the driving context (e.g., vehiclelocation information, such as lane identifier, road type, trafficconditions; weather conditions; etc.), the operator identifier for thevehicle's operator (e.g., determined using the method disclosed in U.S.application Ser. No. 15/642,094 filed 5 Jul. 2017, incorporated hereinby this reference, retrieved, or determined using any other suitablemethod), a vehicle identifier for the vehicle, the object's identifier,the near-collision direction, or other factor values.

The method can optionally include storing associated data with thenear-collision event parameters (e.g., FIG. 11). Associated data caninclude: underlying data (e.g., data from which the near-collision eventwas detected); sensor data sampled preceding, during, or following thenear-collision event (e.g., a sensor stream segment sampled within atime window of the near-collision event), sensor data sharing aparameter with the near-collision event (e.g., collected during the samedriving session), or any other suitable data. The stored associated datacan be selected based on the near-collision event label (e.g., whereindifferent labels can be associated with different data types and/ortimeframes), be a predetermined set of data (e.g., only camera imagesand kinematic sensor data sampled within a predetermined timeframe), orbe any other suitable data. In one example, data describing thenear-collision event context (e.g., a segment of the proximity sensordata, exterior-facing camera video, or other sensor data stream ortimeseries) and data associated with the operator reaction (e.g., asegment of the interior-facing camera video, vehicle control inputsensors, user response data, or other sensor data stream or timeseries)can be stored in association with the near-collision event parameters.The stored segment of the sensor data stream or timeseries can be thesensor signals sampled a predetermined time window before, after, and/orduring the near-collision event timeframe, be the sensor signals for theentire driving session, or be any other set of sensor signals. The timewindow can be predetermined; vary based on the risk map's highest riskscore, risk score distribution, or other risk parameter; selected basedon whether a collision occurred; selected based on the near-collisioncause or class, or otherwise determined. The parameters, data, or otherinformation can be stored by the on-board system, by a remote system(e.g., wherein the information can be transmitted in near-real time orasynchronously, such as when a given connection type is detected, fromthe processing or recordation system to the remote system), or by anysuitable system.

The method can optionally include labeling the near-collision event,which functions to ease subsequent aggregate near-collision eventprocessing. Additionally or alternatively, the driving session (e.g.,session identifier, session data, etc.) can be labeled as anear-collision event, with the near-collision event label, or otherwiselabeled. The near-collision event can be automatically labeled (e.g., bya remote computing system, by the vehicle, etc.), manually labeled, orotherwise labeled. The near-collision event is preferably labeledasynchronously, but can alternatively be labeled in real- or near-realtime. The label can be: a near-collision event class or type (e.g.,collision with a lead vehicle, side collision with a bicycle,pedestrian, signage, or curb, reverse collision, etc.; wherein thenear-collision event can be classified based on the substantiallyconcurrently recorded data), a risk severity (e.g., very close miss,etc.), a near-collision cause, or any other suitable set of descriptors.

The method can optionally include determining the cause of thenear-collision event (e.g., the set of precipitating event(s)) S230. Thecause can be stored in association with (e.g., used to label) dataunderlying the near-collision event detection or otherwise used. Thecause can be used to: filter, cluster, or otherwise manage thenear-collision events, assign fault in a collision, calculate a driverscore (e.g., wherein near-collision events that are not caused by theoperator can be excluded from the operator score calculation),automatically identify or fill out a report (e.g., insurance report,accident report, etc.), adjust the respective risk map's influence on anaggregate collision risk map for a geographic location, determine whichtraining data should be used for autonomous vehicle control module orrisk map module training (e.g., filter out data for near-collisionevents caused by the driver, identify edge cases, etc.), determinenotification or automatic control parameters, or otherwise used.

In a first variation, the cause can be determined based on subsequentvehicle behavior relative to a predetermined risk map. For example, thevehicle operator can be determined as the cause when the vehicletrajectory is directed toward (e.g., intersects, is within apredetermined distance or angular region of, etc.) a predeterminedhigh-risk risk map region, or when the vehicle subsequently moves towardthe predetermined high-risk risk map region. In a second example, theobject can be determined as the cause when the vehicle trajectory is notdirected toward the high-risk risk map region but the high-risk risk mapregions proximal the vehicle increase over time (e.g., due to objectmovement). However, the cause can be otherwise determined based onsubsequent vehicle behavior.

In a second variation, the cause can be determined based on the RAM. Inone embodiment, the cause can be determined based on individualparameters of the RAM's model. For example, the cause can be determinedbased on the independent parameter with the highest weight, the highestweighted value (e.g., product of the weight and factor value), thehighest factor value highest influence on the risk score, the lowestvalue of the above, or any other suitable parameter. The cause can bethe parameter itself, a cause associated with the parameter or factor, acause associated with the most influential parameters (e.g., parametercombination, parameter value combination, etc.), or be otherwisedetermined. In a second embodiment, the cause can be determined based ona derivative or integration of the RAM or resultant risk metrics (e.g.,risk maps). In a third embodiment, the cause can be determined based onthe series of RAMs that are selected over time for a given vehicle(e.g., wherein the temporal pattern of RAMs are associated with apredetermined cause), or based on the series of risk metrics (e.g., riskmaps) generated by the respective RAMs. However, the cause can beotherwise determined from the RAM and/or resultant risk metrics.

In a third variation, the cause can be determined from analysis of thedata associated with the near-collision event. In a first embodiment,the method includes detecting the near-collision event with the riskmap, retrieving data recorded within a predetermined time window of thenear-collision event (e.g., including all, some, or none of the dataused to generate the risk map), and analyzing the data for the cause.The data can include external video, internal video, proximity sensordata, vehicle sensor data, or any other data. The data can be analyzedusing pattern matching (e.g., wherein different signal patterns areassociated with pre-associated causes), classification, neural networks,rules, decision trees, Bayesians, support vectors, genetic programs, orany other suitable method. In a specific example, the interior-facingcamera stream recorded before, during, and/or after the near-collisionevent (e.g., a segment of the internal video encompassing or otherwiserelated to the near-collision event) can be analyzed to determinewhether the driver saw the object before collision. In a second specificexample, the location system measurements can be analyzed to determinewhether the driver was driving within the lane boundaries. In a thirdspecific example, the object's sensor measurements before, during,and/or after the near-collision event can be analyzed to determine theobject fault contribution. However, the cause can be otherwisedetermined.

The method can optionally include verifying the near-collision event(e.g., before storing the near-collision event or labeling the drivingdata). In one variation, verifying the near-collision event includesdetecting the same near-collision event (e.g., with the same or similarparameters, timestamp, etc.) with separate detection variants (e.g.,disclosed above) or detection systems. For example, a near-collisionevent can be detected when the risk map includes a risk score above athreshold value, and can be verified in response to determination thatan object (located at the sub-region with the high risk score) is movingtoward the vehicle, based on proximity sensor signals. In a secondexample, a near-collision event can be detected when the risk mapincludes a risk score above a threshold value (e.g., at a first time,based on signals sampled at a first time), and can be verified whensensor signals associated with evasive maneuvers (e.g., sampled before,after or during the first time) are also identified.

The method can optionally include determining the operator behaviorassociated with the near-collision event S240. Examples of operatorbehaviors include: evasive maneuvers (e.g., steering, braking,acceleration, a combination of control inputs that approach the limitsof vehicle capabilities, etc.), pre-incident maneuvers, attentiveness,or any other suitable behavior. The operator behavior is preferablydetermined from sensor signals monitoring the operator volume, such asan interior-facing camera, operator-facing camera, or vehicle controlinput sensors (e.g., pedal sensors, steering wheel sensors, etc.), butcan additionally or alternatively be determined from sensor signalsmonitoring the exterior volume or any other suitable sensor. Determiningthe operator behavior can include: identifying the operator behavior inthe sensor signals, identifying the sensor signals describing theoperator behavior, classifying or labeling the operator behavior (e.g.,good, bad, safe, unsafe, etc.), or otherwise processing the operatorbehavior. The operator behavior can be determined using: a classifier, apattern matching system, a set of rules (e.g., signals sampled that areby a predetermined set of sensors within a predetermined time window areassociated with the operator behavior), or otherwise determined. Theoperator behavior can be stored with the respective near-collision eventinformation, used to determine the respective operator's driver score,or otherwise used.

The method can optionally include acting in response to detection of thenear-collision event S250, which can function to use the detectednear-collision event and/or parameters thereof in one or moreapplications. For example, automatic driver notifications can bepresented, automatic vehicle control can be performed, virtualsimulations can be generated, or any other suitable action can beautomatically taken in response to near-collision event detection.

In a first variation, the method can include generating a notificationbased on the detected near-collision event and/or associated factors orparameters (e.g., cause). The notification can be generated and/ortransmitted before, during, or after the near-collision event. Thenotification can be for a user (e.g., include a recommendation ornotification for management entity, the operator, an insurance entity,etc.), vehicle, or other endpoint. The notification can be automaticallygenerated and/or presented, but can alternatively be otherwisecontrolled. In a first example, a notification, such as a flashinglight, audio notification (e.g., warning sound), vehicle componentactuation (e.g., seat vibration, steering wheel vibration, etc.), orother notification can be presented to the user in response to detectionof a near-collision event. In a second example, the vehicle can beautomatically controlled to avoid an imminent collision. For example, inresponse to imminent collision detection, the accelerometer can beremapped, the brakes automatically applied, the speed automaticallyreduced, or the wheels automatically turned (e.g., to enter or follow anautomatically determined escape route, example shown in FIG. 16, whichcan be determined from the concurrent risk map or otherwise determined).In a third example, driving behavior recommendations (e.g., coachingrecommendations) can be transmitted to the operator. Subsequent driverbehavior can optionally be subsequently monitored for a predeterminedduration for positive (e.g., decreased near-collision event frequencies)or negative (e.g., increased near-collision event frequencies) changesin driving behavior. A different recommendation can be provided to thedriver when negative changes are determined, while the same or similarrecommendation can be provided to other operators (e.g., with similarrisk profiles, driving habits, causes, etc.) when positive changes aredetermined (e.g., cause occurrence frequency falling below a thresholdfrequency during a predetermined time window). However, any othersuitable action can be taken.

In a second variation, the method can include sending the associatedsensor measurements to a remote computing system. The associated sensormeasurements can include the sensor measurements underlying thenear-collision event detection, sensor measurements recorded apredetermined time duration preceding the near-collision event, or anyother suitable sensor measurement.

In a third variation, parameters of the near-collision events (e.g.,frequency, severity, type, cause, etc.) detected for a given driver(e.g., identified by the driver's phone, driver's biometrics, etc.) canbe used to assign a driver score indicative of the driving risk orsafety to the driver. The driver score can subsequently be used todetermine: parameters of the risk map (e.g., monitored region, riskscore determination, etc.), parameters of the RAM, conditions triggeringnear-collision event detection, insurance premium determination, driversfor driving coaching courses, which training data should be used forautonomous vehicle control module training, or otherwise used.

In a fourth variation, the vehicle and/or object actions before, during,and/or after the near-collision event can be extracted, analyzed, andutilized. In a first example, the vehicle behavior, object behavior,driver actions, or other parameters preceding the near-collision eventcan be extracted and used to refine risk map generation, determine thecause of the near-collision event, assign fault to the driver (e.g.,determine fault percentage), or otherwise used. This information canadditionally or alternatively be used to identify weak areas in adriver's driving skillset, which can be targeted for coachingimprovement. In a second example, the vehicle trajectory or driveractions taken to avoid the collision can be extracted and used to coachother drivers in similar situations, used to anticipate the reactions ofsimilar objects in a similar situation (e.g., anticipate how apedestrian will react in a similar situation), used in determiningautonomous vehicle responses in similar situations, or be otherwiseused. However, the near-collision detection event and/or parametersthereof can be otherwise used.

In a fifth variation, the method includes training modules based on thenear-collision event information set S280. The near-collision eventinformation set (e.g., plurality of near-collision event data) caninclude the near-collision events (preferably labeled, alternativelyunlabeled) and/or associated data that are aggregated for a givenoperator, vehicle, user population, location, timeframe (e.g., recurrenttimeframe, single timeframe, etc.), all method instances (e.g., for aplurality of enabled or un-enabled vehicles), or other parameter. Theinformation set can be used to generate a training set (e.g.,supervised, alternatively unsupervised) for module training (e.g.,calibration, updating, etc.). Modules that can be trained using thetraining set include: the monitored region size determination module,the monitored region shape determination module, the monitored regionorientation determination module, the risk determination module, therisk equation selection module, autonomous vehicle control module (AVcontrol module), or any other suitable module. The trained module ispreferably subsequently used in the method, used to control a secondaryvehicle (e.g., the AV control module), or otherwise used.

In a first example, the near-collision event information set can befiltered to include or exclude a predetermined set of causes, whereinthe AV control module can be trained on the filtered set. For example,the driving trajectory, trajectory classification, or driver responseassociated with a near-collision event can be used to filter gooddriving trajectories or responses from bad driving trajectories orresponses, or otherwise differentiate between different drivingtrajectories (example shown in FIG. 17). Good driving trajectories caninclude trajectories or driver responses that are: generated by driverswith high driver scores (e.g., above a manually or automaticallydetermined threshold score), responsive to near-collision events thatwere not caused by the driver, successful at mitigating or avoiding acollision, did not result in subsequent regions of high collision risk,or otherwise characterized. These driving trajectories can besubsequently used to control autonomous vehicle traversal, be used totrain or develop autonomous vehicle control modules, or be otherwiseused.

In a second example, operator-caused near-collision events can befiltered out of the near-collision event information set used to trainthe AV control module. In a third example, the information set can befiltered to identify edge cases (e.g., rare occurrences, as manuallydetermined or determined based on the label occurrence frequency in theinformation set), wherein the edge cases can be used to train the AVcontrol module. In a fourth example, the information set includes datadescribing the near-collision event context (e.g., proximity sensordata, exterior-facing camera video, or other sensor stream segment,etc.) and data describing the operator's reaction (e.g., interior video,vehicle control input sensors, or other sensor stream segment, etc.). Inthis example, the information set can be filtered for near-collisionevents with successful evasive maneuvers (e.g., near-collision eventsthat were not followed by a collision event within a predeterminedtimeframe), desired behaviors (e.g., safe maneuvers, etc.), good drivers(e.g., operators with high driver scores), a predetermined set ofcauses, or other parameters (e.g., associated with desired evasivemaneuvers), wherein the identified near-collision event information(e.g., data for the precipitating causes and user responses) can be usedto train the AV control module. In a fifth example, the near-collisionevent information set can be filtered to include specific roadgeometries (e.g., roundabouts, intersections, etc.), wherein the AVcontrol module can be trained on the filtered set.

However, the near-collision event can be otherwise acted upon, asdescribed above.

The method can optionally include acting on the stored risk maps, whichfunctions to characterize the risk for a given geographic location,driving session (or segment thereof), vehicle, driver, or other dataobject. The risk maps are preferably processed and/or acted upon by theremote computing system (e.g., wherein the risk maps or parametersthereof are transmitted to the remote computing system from thevehicle(s)), but can alternatively be processed and/or acted upon by aon-board vehicle system, a secondary vehicle system, or any othersuitable system.

In a first variation, a collision risk map for a given geographiclocation or region can be generated from the risk maps and/ornear-collision events aggregated across a population of vehicles. Thecollision risk map can be generated for and/or stored in associationwith each of a plurality of locations, a single location, or anylocation set. The collision risk map can include the risk (e.g., fromthe risk maps), collision, and/or near-collision hot-spots in real- ornear-real time, for a predetermined recurrent time (e.g., time of day),or for a predetermined time duration (e.g., across all time). In oneexample, the collision risk map can reflect a near-real time map oftraffic dynamics, which can be used for dynamic route planning,increased ADAS sensitivity, or otherwise used by a secondary vehicle. Ina second example, the collision risk map can be used for infrastructuremanagement or improvement. In a specific example, collision hot-spotscan be targeted for driver visibility improvements, traffic lane dividerinsertion, or other infrastructure improvements. In a third example, thecollision risk map can be used to adjust the parameters of the risk map,parameters of the RAM (e.g., which parameters are included, theparameter value ranges, the parameter weights, the model itself, etc.),conditions triggering near-collision event detection, or otherwisefeeding back into the near-collision detection method. For example, theregion monitored for near-collision events can be dynamically increasedin locations with a high frequency of near-collision events.

In one embodiment of the first variation, the method includesaggregating the risk maps for a given geographic location or region(e.g., in real time, for all time, for a recurrent time frame such as 8a on a Monday, etc.) to generate a collision risk map for the geographiclocation S260 (e.g., FIG. 15). In one example, the method can includereceiving a collision risk map request with a location identifier for asecondary vehicle (e.g., from the secondary vehicle, navigation system,or other endpoint) and/or retrieving and transmitting the risk mapassociated with the location identifier to the secondary vehicle orassociated system for secondary vehicle navigation, operation, or otheruses. For example, the ADAS for secondary vehicles can automaticallyslow the vehicles down in high-risk areas. The secondary vehicle can bea vehicle within the enabled vehicle population (e.g., capable ofperforming the system, that includes the computing system, etc.), anun-enabled vehicle outside of the enabled vehicle population (e.g.,incapable of performing the system, lacking the computing system, etc.),or be any suitable vehicle.

In a second variation, the method includes identifying escape routes forthe vehicle based on the risk map S270, wherein the escape routes can beused to generate operator notifications (e.g., haptic, optical, etc.notifications), automatically control vehicle operation (e.g., to travelalong the escape route), or otherwise used. An escape route can be: atrajectory passing through regions with risk scores below a thresholdvalue, a trajectory with an average or aggregate risk score less than athreshold value, or be otherwise defined. However, the risk map can beotherwise used.

Embodiments of the system and/or method can include every combinationand permutation of the various system components and the various methodprocesses, wherein the method processes can be performed in any suitableorder, sequentially or concurrently.

As a person skilled in the art will recognize from the previous detaileddescription and from the figures and claims, modifications and changescan be made to the preferred embodiments of the invention withoutdeparting from the scope of this invention defined in the followingclaims.

1. A method for near-collision analysis, comprising, at an on-boardsystem mounted to a vehicle: recording an external video with anexternal-facing camera of the on-board system; determining obstacleparameters for an obstacle detected in the external video; generating aspatial risk map for the vehicle based on the obstacle parameters, thespatial risk map comprising a risk score for each of a plurality ofspatial positions relative to the vehicle wherein the risk score isdetermined using: a parametric model comprising a set of Gaussianmodels; the obstacle parameters; and driver parameters extracted from aninternal video recorded by an internal-facing camera of the on-boardsystem; detecting a near-collision event when a risk score within thespatial risk map exceeds a risk threshold; labeling the near-collisionevent with a label determined based on the driver parameters;determining a cause of the near-collision event, comprising identifyingan independent parameter of the parametric model with a highest weightedvalue; and transmitting the label to a remote computing system.
 2. Themethod of claim 1, further comprising determining secondary obstacleparameters for a second obstacle detected in the external video, whereineach risk score is calculated based on the obstacle parameters and thesecondary obstacle parameters.
 3. The method of claim 1, wherein thecause of the near-collision event is determined based on the driverparameters, wherein the label comprises the driver cause.
 4. The methodof claim 1, further comprising transmitting a segment of the externalvideo and a segment of the internal video to the remote computingsystem, in association with the label.
 5. The method of claim 4, furthercomprising: determining a driver response to the near-collision eventfrom the internal video; aggregating driver responses to near-collisionevents for a plurality of drivers; identifying good drivers associatedwith high driver scores; determining the driver responses of the gooddrivers to near-collision events sharing a common label; and training avehicle control system based on the driver responses of the gooddrivers, the associated segment of the external video, and theassociated segment of the internal video, wherein the vehicle controlsystem controls an autonomous vehicle.
 6. A method for near-collisionanalysis, comprising: sampling an external image with an external-facingcamera mounted to a vehicle; determining obstacle parameters for anobstacle detected from the external image; generating a spatial risk mapfor the vehicle based on the obstacle parameters, the spatial risk mapcomprising a risk score for each of a set of spatial positions relativeto the vehicle, wherein the risk score is determined using: a parametricmodel comprising a set of Gaussian models; the obstacle parameters; anddriver parameters associated with a driver of the vehicle; detecting anear-collision event when a risk score within the spatial risk mapexceeds a risk threshold; labeling the near-collision event with a labeldetermined based on the driver parameters; transmitting the label to aremote computing system; and determining a cause of the near-collisionevent, comprising identifying an independent parameter of the parametricmodel with a highest weighted value.
 7. The method of claim 6, wherein aprocessing system on-board the vehicle detects and labels thenear-collision event.
 8. The method of claim 6, further comprisingdetermining secondary obstacle parameters for a second obstacle detectedfrom the external image, wherein the spatial risk map is furthergenerated based on the secondary obstacle parameters.
 9. The method ofclaim 6, wherein the driver parameters comprise a driver responsedetermined from sensor signals sampled during the near-collision event.10. The method of claim 9, wherein the sensor signals comprise aninternal image sampled by an internal-facing camera mounted to thevehicle.
 11. The method of claim 9, further comprising: aggregatingdriver responses to near-collision events for a plurality of drivers;identifying good drivers associated with high driver scores; determiningthe driver responses of the good drivers; and training a vehicle controlsystem based on the driver responses of the good drivers, wherein thevehicle control system controls an autonomous vehicle.
 12. The method ofclaim ii, further comprising: associating the near-collision event witha driver of the plurality; and for each of the plurality of drivers,determining a driver score based on a history of near-collision eventsassociated with the driver.
 13. The method of claim 12, furthercomprising coaching the driver based on the history of near-collisionevents associated with the driver.
 14. The method of claim 6, whereineach risk score is further determined based on the driver parameters.15. The method of claim 14, wherein each risk score is calculated usinga parametric model, the driver parameters, and the obstacle parameters.16. The method of claim 15, wherein the parametric model comprises a setof Gaussian models. 17-19. (canceled)
 20. The method of claim 6, furthercomprising: determining a near-collision location associated with thenear-collision event; and aggregating near-collision locations ofnear-collision events from a plurality of vehicles into a geographicrisk map.